Estimating total net primary productivity of managed grasslands by a state-space modeling approach in a small catchment on the Loess Plateau, China

Abstract

Managed grasslands are important for stabilizing soil and reducing soil erosion on sloping lands. In order to obtain information for better grassland management and soil protection at small scales, managed grassland total net primary productivity (TNPP) data were collected and analyzed with a first order state-space approach and a classical linear regression approach. The objective was to determine the effects of soil properties and site elevation on managed grassland TNPP. Soil water content (SWC), soil bulk density (BD), saturated soil hydraulic conductivity (Ks), soil temperature (T), soil clay content (CC), soil organic carbon (SOC), soil NO 3–N, soil NH 4–N, soil Olsen phosphorus concentration (OP) and site elevation (SE) data were collected along a 300-m transect in the China Loess Plateau. Soil properties and site elevation were evaluated in bi- and multivariate autoregressive state-space analysis to clarify the key factors affecting the spatial distribution of TNPP. Results show that most of the measured variables contributed to the variation of TNPP. CC and OP were especially helpful in describing the spatial pattern of TNPP. The state-space modeling results were compared with classical statistics methodologies, indicating that the state-space approach described the spatial pattern of TNPP much better than the equivalent classical regression methods. All of the TNPP variation was represented by state-space models that included soil NO 3–N and OP or soil CC and OP. Only 76% of the variance of the TNPP was represented by classical statistics analysis because the classical statistics did not include sampling position and assumed sample independence. State-space models are recommended for studying spatial relations between vegetation and soil variables in natural soil-plant systems on the China Loess Plateau.